MétaCan
Menu
Back to cohort

Dynamic Pulse-Positioning for a Single-Stage Isolated AC-DC Converter

2022· article· en· W4310502175 on OpenAlexafffund
Vishwa Perera, Juan Zuniga, John Salmon

Bibliographic record

Venue2022 IEEE Energy Conversion Congress and Exposition (ECCE) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransformerDelta-wye transformerForward converterFlyback converterElectrical engineeringEnergy efficient transformerPulse-width modulationAC powerDistribution transformerEngineeringElectronic engineeringControl theory (sociology)VoltageBoost converterComputer science

Abstract

fetched live from OpenAlex

The control of a grid connected single-stage isolated ac-dc converter is presented where the transformer power flow occurs at high frequency where the transformer primary has both low and high frequency current components. The converter primary, used to control the low frequency grid current, creates a high frequency transformer switch mode voltage pattern whose pulse widths are determined by the instantaneous modulation index used by the primary side converter to control the grid current. The secondary side converter matches the primary side transformer voltage switching patterns and introduces a custom cycle-by-cycle phase shift to control the high frequency power flow between the transformer primary and secondary windings. This coordination of pwm pulses is done in such a way as to lower the flow of high frequency reactive currents when compared to using a standard phase shift control approach. Hence, the rms of the high frequency currents are lowered together with their associated conduction losses. The operation of the transformer isolating converter is verified with simulations and experimental results using a 2.2kW prototype converter.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.204
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

Same venue2022 IEEE Energy Conversion Congress and Exposition (ECCE)Same topicAdvanced DC-DC ConvertersFrench-language works237,207